dijkstra_shortest_paths_no_color_map_no_init from boost
library with the class djk_max_visitor. Do you think I can just extend the
class adding the examine_edge method?
Sure, why not...
Best,
Tiago
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if you only want to compute shortest
paths or distances, then you should use the specialized functions.
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[e32] = 5
edge_weights[e03] = 1
state = minimize_nested_blockmodel_dl(g, overlap=False,
state_args=dict(recs=[edge_weights], rec_types=['discrete-geometric']) )
print('Should be 2, but is ',state.levels[0].B)
Why to you think there should be 2 groups instead of 1?
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he 'eprops' parameter), which requires
a numpy array with three values, (source, target, weight).
However, if you do not have enough RAM to hold the entire graph +
weights into memory, this is also not going to work.
Best,
Tiago
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approved your user now; you should be able to log in.
I had to turn on manual approvals due to the huge number of spam
accounts being created. I'm working on an alternative solution to this.
Best,
Tiago
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Am 29.01.21 um 09:04 schrieb Binon:
I am working to draw the vertices included in the local clustering algorithm.
But, in the documentation only coefficient values are shown. Please help!
Please be more specific, otherwise it's impossible to say anything.
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a shared library component. Otherwise, at
the moment it would be a significant effort for me to try this myself...
Best,
Tiago
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strange. I can reproduce a segfault with the
instantiation of MeasuredBlockState (not copying), but only on buster,
not on unbuntu or any other distro.
Could you please open an issue for this in gitlab, so I can keep track,
and investigate in more detail?
Best,
Tiago
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speed improvement by using
pypy, this would not be achieved for graph-tool, since it is implemented
in C++.
Best,
Tiago
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#graph_tool.draw.radial_tree_layout
If you want support for Reingold-Tilford in particular, please open an
issue in the website, and I will implement it when time permits.
Best,
Tiago
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he key is indeed expired or if it is an issue
on my side?
Just try downloading the key again from the keyserver, and you should
get an updated one.
Best,
Tiago
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://en.wikipedia.org/wiki/K_shortest_path_routing
If you open an issue in the website with a feature request, I will
implement it when I find the time.
Best,
Tiago
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gt 2.29 from conda-ostrokach.
Please upgrade! There has been up-to-date graph-tool packages in
conda-forge for a while now.
Best,
Tiago
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Am 05.12.20 um 13:07 schrieb Ben Hong:
I found that the returns of counts and bins have different dimensions , it
seems strange.
Did you bother to read the documentation?
It returns the bin counts and the bin *edges*. If we have N bins, there
are N+1 bin edges.
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, in the case of discrete distributions, the derivative term is not
applicable.
Best,
Tiago
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ecause of a boundary condition: The last point does not
need to be specified, just as the first one. If it's given, it's
ignored. The function get_hierarchy_control_points generates the final
point because it's slightly easier in the code.
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for the vector, sometimes 50 or so.
The documentation clearly states that it's 6 values *per spline
segment*, of which there can be an arbitrary amount.
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is actually misleading in this context, since the
value refers to a log-density rather than a log-probability.)
Best,
Tiago
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Description: Op
ed to draw the edge. Each spline
segment requires 6 values corresponding to the (x,y) coordinates of the
two intermediary control points and the final point.
Bézier splines are defined as such:
https://en.wikipedia.org/wiki/B%C3%A9zier_curve
Best,
Tiago
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verlapping model.
also, slightly unrelated, is there any way to fit a hierarchical model with
2 levels where the top level is overlapping and the bottom level is not?
No, this is not currently implemented.
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Description: applicatio
an achieve this by using the condensation_graph() function:
https://graph-tool.skewed.de/static/doc/generation.html#graph_tool.generation.condensation_graph
You need only to mark the vertices that need to be merged with the same
property map value.
Best,
Tiago
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nd it will work.
Best,
Tiago
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-graph-tool installed on my computer which runs on Linux
> Mint 19.2 Tina (based on Ubuntu 18.04 LTS, bionic). My graphviz version
> is 2.40.1.
>
> Can anyone reproduce this?
>
> Best regards
> Rolf
>
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upstream, but you seem to have denied
>> my access request so I can't create a merge request. If you think this
>> is fine, could you make the change? In general, how do I fix bugs
>> if/when I find them if access on git.skewed is not available?
>>
>> Thanks and regards,
>> Snehal Shekatkar
>>
>>
>>
>>
>
>
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he actual route is simply to clone the
repository, make the changes to your cloned version, and then do a merge
request.
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_tool.topology.dominator_tree(graph, start, as_graph=True)
That seems too ad hoc for me. I would prefer a more general function
that creates a tree graph from an adjacency vector.
Please open an issue for this, and I will take care when I find the time.
Best,
Tiago
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ger.
See why it's important to share the actual code?
Best,
Tiago
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on-starter.
A side observation: If you are implementing a gt reader in Javascript,
it is not very likely that you will get much speed improvement over
graphml/gml. Significant improvements will only be seen if implementing
in C/C++.
Best,
Tiago
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Desc
s
particular code.
Best,
Tiago
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ontrols the
strength of the attractive force between nodes of the same group.
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e *never* composed of i.i.d. degrees, since
they must fulfill the Erdős–Gallai inequality. For (uniformly) sparse
graphs, most sequences of i.i.d. degrees will fulfill it with high
probability, so we can say the degrees are approximately independent.
But as long as some degrees become large, this is
ou're attempting to generate an impossible graph, so I don't understand
what you were expecting would happen.
Best,
Tiago
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sample the degrees for all vertices first,
and check if the final sequence is graphical. If it's not, then a random
node is selected, and its degree is re-sampled, and the test is done
again. This is repeated, until the sequence obtained is graphical.
Best,
Tiago
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ause I work on a university
> server that is a pain to update regularly in case this was a known bug.
This is a very old release, and you should definitely upgrade.
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> ___________
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ption to reduce the variance may be to rank the values encountered,
and take the rank index as the transformed covariate. YMMV.
Best,
Tiago
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ean automatic binning; unfortunately this is not yet
implemented in the library. It's in my TODO list, but you know how that
goes.
Best,
Tiago
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Am 23.08.20 um 13:10 schrieb Kerr Avon:
> When a pydev project includes an import of graph_tool, Eclipse consumes very
> high cpu cycles by percentage.
This has nothing to do with graph-tool. Please report this problem to
the pydev project.
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aspect" is very slow and does not improve the
> plots much. Examples are available here:
Indeed, we still need some form of non-overlapping heuristics in
graph_draw().
I'll leave graphviz_draw() for the time being.
Best,
Tiago
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D
lds, not function
> pointers) and the test case works again.
Thanks for the fix, and the patch in gitlab, which has been merged.
Best,
Tiago
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>
>
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(If you want to help me not forget
it, please open an issue for it in the website.)
Best,
Tiago
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uch of an improvement
anyway, since the reason why it runs faster is mostly because it does
not do any extra computation, such as collecting history.
The version you have is basically as fast as you can get. The only thing
I would suggest is to move the line
s = state.get_state()
outside of th
most appropriate version is the one with the constant
outside the power, as written in the documentation. I'll fix the code
shortly.
Best,
Tiago
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nough,
but I would encourage you to experiment and draw your own conclusions.
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Markov chains. Could you kindly point me
> to the actual algorithm?
This is the usual edge-swapping algorithm that has been discovered and
re-discovered many times since the 50s. You can find a good description
in the recent paper: https://arxiv.org/abs/1608.00607
Best,
Tiago
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negative_weights=True)[1]:
print(e, g70.edge_index[e], g70.ep.weight[e])
which will print:
(0, 2) 2 -2.0
Note that it's always obvious which of the parallel edges get selected:
it's always the one with the smallest weight, otherwise it would not be
part of the sh
Am 20.07.20 um 13:08 schrieb Snehal Shekatkar:
> If I set "model = 'configuration'" and "parallel_edges = True,
> self_loops = True", will "n_inter = 1" suffice?
Yes.
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I would need to check.
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to cancel certain parts of the
likelihood, without paying proper attention to issues of normalization,
etc, and expect things to behave consistently.
In other words, I do not fully agree with the alpha parameter of Aicher
et al.
> Is it possible to use LatentMultigraphBlockState() with a weigh
sense under the model, because a random multigraph
model with the same degree sequence would yield a larger number of
connections between the hubs, and between the nodes with smaller degree.
See an explanation for this in this paper: https://arxiv.org/abs/2002.07803
Best,
Tiago
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want to express the uncertainty of the mean, not of the
distribution.
> It is just a curiosity because I never saw that measurement :)
https://en.wikipedia.org/wiki/Standard_deviation#Standard_deviation_of_the_mean
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: model.vp['node_type'], 'pclabel': model.vp['node_type']})
>
> then I will end up with a partition with G blocks. But what I want is a
> partition in which the type 1 nodes are divided into G1 blocks and the type
> 2 nodes into G2 blocks, where I specify G1 and G2.
This is currently not yet
o draw:
>
> gt.draw_hierarchy(state, layout='bipartite')
>
> but I get this error
In order for us to investigate further you need to provide us with a
minimal working example, not only the error message.
Best,
Tiago
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pr.a.argsort()
Best,
Tiago
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the specific website permission, so I hope that another
> person who has access can post the issue in order for it to get fixed!
It's a public website, but you need to login. You can use your account
on google, github, etc, or you can create a new one.
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indeed missing.
Thanks for spotting it!
Please open an issue in the website, and I'll fix it for the next release.
Best,
Tiago
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Is there a reason why I get that figure?
Hard to say without a minimal working example. Try saving the network to
disk, and loading it again from a newly started interpreter.
I tried on my machine, and I got 4.5 GB. As I explained, std::vector<>
may over-commit the allocations.
--
0 bytes = 3.01 GB
In practice you will need a little more, since std::vector<> tends to
over-commit.
Best,
Tiago
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communities, and outside. So those two models do not fit
together.
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e description length is useful to select the best fitting
model, but not to tell if they give similar answers.
Best,
Tiago
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if
it's using fork().
You should disable OpenMP in the beginning of your code with:
import graph_tool as gt
gt.openmp_set_num_threads(1)
Best,
Tiago
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l be performed. A serial run
> works normally (without multiprocessing).
>
> Would anyone have a tip?
>
> Best regards,
>
> Ronaldo Alves
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Am 30.06.20 um 12:32 schrieb Tiago de Paula Peixoto:
> There are no packages for xenial, since it's outdated and has reached
> end of life. You should upgrade to a supported ubuntu version (the
> current LTS is 20.04, but 18.04 will work too).
I'm sorry, this is incorrect, xenial has no
outdated and has reached
end of life. You should upgrade to a supported ubuntu version (the
current LTS is 20.04, but 18.04 will work too).
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ion of the node? I checked the references, but I
> still couldn't understand.
> Can you help me?
It's right there in the documentation:
r : ``float`` (optional, default: ``0.``)
Spontaneous infection probability.
Best,
Tiago
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Desc
ostreams
>
>
> How can I solve this?
> Thanks!
>
>
>
>
>
> --
> Sent from: https://nabble.skewed.de/
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mis graph object to the union graph
The documentation of "copy_property" says: "The optional parameter ``g``
specifies the source graph to copy properties from (defaults to self)."
Since this not owned by the same graph you need to do:
ug.copy_property(src=g.vp.label, g=g)
(I wil
need to use `Graph.copy_property()`.
Please show me an example of where you do something that is _supposed_
to work, so I can say what the problem is.
Please send complete and minimal examples.
Best,
Tiago
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cated). This will create parallel edges for those that appear in
both graphs, which you can remove with remove_parallel_edges(). You can
then copy any edge property map from either g1 or g2 by calling
Graph.copy_property() on the union graph.
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Descr
has been
measured. Before we talk about how to do anything efficiently, can you
first explain what "get around" this issue means? How do you expect two
graphs with a different set of edges to share the same edge property map?
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he property map?
Not without looping and assiging them one by one.
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ted. You will have to do the
binning yourself. But it's not that difficult...
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st graph-tool 2.32 was not built for 19.04 because it has been
discontinued (reached end of life). Maybe the error above is because you
installed a package for another ubuntu version?
Best,
Tiago
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Am 05.06.20 um 12:17 schrieb Enrique Castaneda:
> Still the same errors (No confirmation email and account blocked while
> trying with bitbucket)
The email confirmation should be working now.
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3.nabble.com/file/t496242/2020-06-05_06_04_32-Window.png>
>
>
> Am I doing something wrong or is there another process to register the site.
Could you please try again?
I have been doing maintenance on the server, which has just been moved
to a new location.
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spy? Or do you see a more direct way?
It would be valid as a reasonable approximation, since the models become
equivalent as the number of edges becomes sufficiently large.
Best,
Tiago
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ot;infinite weight"
edges using an edge filter.
Best,
Tiago
Ps. I note you are using Python 2, but current versions of graph-tool
only support Python 3.
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I was figuring that this should also be possible when fitting the model.
Yes, this exactly what is described in the above paper, and what is
implemented in graph-tool.
Best,
Tiago
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on is ill posed.
It would be like comparing a discrete geometric distribution with a
Gaussian; they will always be different, as they do not even have the
same support.
Best,
Tiago
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_
ented in the
> graph-tool library? I can't seem to find this implementation in the
> documentation.
Unfortunately, this is not currently automatized in graph_tool. It has
been in my todo list for a long time, but I never manage to get to it.
Sorry!
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the word groups (topics) produced by the hSBM model?
Yes, take a look at the "get_hierarchy_tree()" function that returns you
the tree used in the layout. It returns a 'label' property map that maps
the vertex of that tree to the corresponding group label.
Best,
Tiago
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possible with
condensation_graph(). Please open an issue in the website with a feature
request, and I will implement it when time permits.
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graph-tool, I do
not see any warnings.
As I already told you two times before, these warnings come from outside
of graph-tool. Fix your environment if you do not want to see them, or
disable them. Just don't shoot the messenger.
Best,
Tiago
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Des
our version of) boost.
Best,
Tiago
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should work.
You should also think about using conda, which does not require
compilation, and is specially well suited for these kinds of restrictive
environments.
(Also note you are trying to compile an older version, the newest one is
2.32)
Best,
Tiago
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trace-limit=0 ${CXXFLAGS}"]
Why?
You can override this with -Wno-all -Wno-extra etc.
Best,
Tiago
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ocations. (But I believe "LIBS" is unnecessary.)
It's all explained in the installation instructions:
https://git.skewed.de/count0/graph-tool/-/wikis/installation-instructions#installing-in-a-specific-location-eg-in-a-home-directory
Best,
Tiago
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; rng_t&) [with Graph = boost::filt_graph int>,
> graph_tool::detail::MaskFilter char, boost::adj_edge_index_property_maplong unsigned int> > >,
> graph_tool::detail::MaskFilter char, boost::typed_identity_property_maplong unsigned int> > > >; State
> = graph_tool::m
/users/lisergey/ClearMap2/graph-tool/src'
> make: *** [Makefile:599: install-recursive] Error 1
This is a bug in GCC that has been fixed in newer versions.
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eing
connected, but where every edge has an integer covariate that is very
high, say on the order of 100, which would not be possible to generate
with model 1 without the whole graph becoming dense.
Best,
Tiago
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Tiago de Paula Peixoto
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Am 22.05.20 um 09:38 schrieb sam:
> import graph_tool as gt
This should be:
import graph_tool.all as gt
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ht
es in the graph, they are taken into account,
since the underlying SBM accepts multigraphs. This is done
automatically, and there is nothing special that needs to be done.
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gra
pile the library (following the
installation instructions for native compilation available in the website).
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t;
>
It's possible to provide your own tree layout for the drawing, but there
is currently no implementation for the layout that you are specifying.
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ould I do? I tried the remove_vertex() API, it seems there
> is something wrong. I ask for help from You.
Please explain what is wrong with minimal and self-contained example
that shows the problem.
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possible to say anything without knowing your network, and what
your expectations were. Why would an increment of around 1 be wrong
for your network?
You can easily test for yourself that the betweenness values for many
networks do not increment by that amount.
Best,
Tiago
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ect the same coefficient for weighted and unweighted. The
> unweighted coefficient for the above example is reasonable.
As is explained in the documentation, the clustering values are
normalized only if the weights are all below 1.
Best,
Tiago
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D
nd 'gamma' parameters of sfdp_layout(),
but this does not always give very good results. YMMV.
Best,
Tiago
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ection
graph_tool.collection.data['karate']
As is explained in the documentation, there is a convenience module
called 'all' which imports all submodules, so you do not need to do this
individually for each one:
import graph_tool.all as gt
gt.collection.data['karate']
Best,
Tiago
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